This demo is to be presented at World of Watson 2016 - "Accelerate Your Data Science Delivery with Integrated Notebooks and IBM BigInsights".
The session link is here: https://www-01.ibm.com/events/global/wow/sessions/#/search/id/DMT-3516
The movielens front end application where users can rate movies is available here: https://movielens.org/.
A screenshot of the movielens user interface can be seen here:
The project is split into a number of different notebooks that focus on specific steps:
This notebook shows you how to provision a BigInsights on cloud cluster on Bluemix.
The cluster is then loaded with the movielens ml-1m dataset using this notebook.
In this step, we import the BigInsights ml-1m dataset into DSX.
In this notebook, we perform some basic exploratory analysis of the ml-1m dataset before we jump into machine learning.
Here we use Spark's Machine Learning Library (MLlib) to train a machine learning model on the data.
In this notebook, we simulate a new user's movie ratings and then use those ratings to predice movies for them.
This notebook exports the model built in the previous notebook.
A scala spark job is then run on BigInsights that loads the model and predicts a rating for a user.
If you have any questions about this project, please contact me at firstname.lastname@example.org